Yingjun Wang1, Zhongyuan Liao1, Shengyu Shi1, *, Zhenpei Wang2, *, Leong Hien Poh3
CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 433-458, 2020, DOI:10.32604/cmes.2020.08680
Abstract Focusing on the structural optimization of auxetic materials using data-driven
methods, a back-propagation neural network (BPNN) based design framework is
developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design variables, the highly
nonlinear relation between the input geometry variables and the effective material
properties is obtained using BPNN-based fitting method, and demonstrated in this work to
give high accuracy and efficiency. Such BPNN-based fitting functions also enable an easy
analytical sensitivity analysis, in contrast to the generally complex procedures of typical
shape and size sensitivity approaches. More >
Login
Register
